Multi-structure atlas and/or use thereof

ABSTRACT

An image data processor ( 106 ) includes a structural image data processor ( 114 ) that employs a multi-structure atlas to segment a region of interest from structural image data that includes tissue of interest and that segments the tissue of interests from the region of interest. The image data processor further includes functional image data processor ( 116 ) that identifies the tissue of interest in functional image data based on the segmented tissue of interest. An image data processor includes a multi-structure atlas generator ( 104 ) that generates a multi-structure atlas. The multi-structure atlas physically maps structure to tissue of interest such that locate the structure in structural image data based on the multi-structure atlas localizes the tissue of interest to the region of interest.

The following generally relates to a multi-structure atlas and/or usethereof in connection with processing structural and/or functionalimaging data. Such imaging data can be generated by a computedtomography (CT), X-ray, Ultrasound (US), magnetic resonance (MR),positron emission tomography (PET), single photon emission computertomography (SPECT), combination (e.g., PET/CT, etc.), and/or otherscanner.

Image data based analysis has provided useful information. For example,image data based lung lobe analysis has been used in connection withidentifying chronic obstructive pulmonary disease (COPD), determininglobe functional characteristics, etc. However, segmenting certainanatomy such as the lung lobes from image data can be a challenging taskas some anatomy such as the lung lobe lines (fissures) can be difficultto localize in image data, even in image data produced from a highresolution scan.

Using a hybrid scanner (i.e., a scanner configured with multiplemodalities) having both structural and functional imaging capabilities(e.g., a PET/CT scanner), if the lung lobes can be segmentedsuccessfully from structural image data, the segmented lung lobes can beregistered and/or otherwise combined with functional image data toidentify the lung lobes in the functional image data, producing imagedata with useful anatomical and functional information of the lunglobes.

With structural and functional image data, anatomy such as the lunglobes have been segmented automatically via a computing system using asingle organ anatomical (e.g., lung lobe) model, which is fitted to theimage data to locate the anatomy of interest. The segmentation can bemodified by a clinician, who can either accept or reject thesegmentation. Such segmentation has also been performed manually by aclinician employing a segmentation software application.

Unfortunately, automated based techniques are challenging and oftenrequire user intervention, which consumes clinician time that couldotherwise be used with patients and/or other tasks. Furthermore, thesignal organ anatomical models are approximations, which may introduceerror in the segmentations, for example, at boundaries and/or elsewhere.As a result of such error, segmentation and/or quantification values maylack accuracy.

Aspects described herein address the above-referenced problems andothers.

In one aspect, an image data processor includes a structural image dataprocessor that employs a multi-structure atlas to segment a region ofinterest from structural image data that includes tissue of interest andthat segments the tissue of interests from the region of interest. Theimage data processor further includes a functional image data processorthat identifies the tissue of interest in functional image data based onthe segmented tissue of interest.

In another aspect, an image data processor includes a multi-structureatlas generator (104) that generates a multi-structure atlas. Themulti-structure atlas physically maps structure to tissue of interest bylocalizing the structure in structural image data based on themulti-structure atlas that localizes the tissue of interest from theregion of interest.

In another aspect, a method includes employing a multi-structure atlasto segment a region of interest from structural image data that includestissue of interest and that segments the tissue of interests from theregion of interest. The method further includes identifying the tissueof interest in functional image data based on the segmented tissue ofinterest.

In another aspect, a method includes generating a multi-structure atlas.The multi-structure atlas physically maps structure to tissue ofinterest such that locate the structure in structural image data basedon the multi-structure atlas localizes the tissue of interest to theregion of interest.

The invention may take form in various components and arrangements ofcomponents, and in various steps and arrangements of steps. The drawingsare only for purposes of illustrating the preferred embodiments and arenot to be construed as limiting the invention.

FIG. 1 schematically illustrates a computing system, including amulti-structure atlas generator and an image data processor, inconnection with multiple imaging systems.

FIG. 2 schematically illustrates an example of the multi-structure atlasgenerator of FIG. 1.

FIG. 3 schematically illustrates an example of the image data processorof FIG. 1.

FIG. 4 illustrates an example method for determining a multi-structureatlas.

FIG. 5 illustrates an example method for employing the multi-structureatlas of FIG. 4.

FIG. 6 illustrates an example lung lobe-rib cage atlas.

The following describes an approach to localize difficult to locatetissue in image data based on a multi-structure atlas. The atlasincludes easier to locate and segment structure spatially or physicallymapped to the location at or near the tissue. This allows for localizingthe tissue based on the locating the structure via the atlas. Oncelocalized, the tissue can be segmented more accurately, relative tosegmenting without first localizing, and the segmented tissue ofinterest can be used to identify the tissue in functional image data.

Initially referring to FIG. 1, a computing system 100 is schematicallyillustrated in connection with multiple imaging systems 102. Thecomputing system 100 includes a multi-structure atlas generator 104 andan image data processor 106.

The multi-structure atlas generator 104 and/or the image data processor106 can be implemented with one or more computing processors of one ormore computing devices executing one or more computer readableinstructions encoded on computer readable storage medium such asphysical memory and/or other non-transitory medium, and/or computerreadable instructions carried by a signal, carrier wave and/or othertransitory medium.

The multi-structure atlas generator 104 generates a multi-structureatlas from atlas generating structural image data. The term“multi-structure atlas” as utilized herein refers to an atlas thatincludes two or more different structures (e.g., rib and lung) in theiranatomical frame of reference with respect to each. As such, thedifferent structures are spatially or physically mapped to each other.An atlas including only a single anatomical organ (e.g., lung only) isnot a multi-anatomical structure atlas.

As described in greater detail below, in one instance, themulti-structure atlas generator 104 generates a multi-structure atlasbased on structure that is easier to locate and segment in structuralimage data and tissue of interest that is more difficult to locate andsegment in the structural image data, where the structure spatially orphysically maps to the tissue of interest such that locating structurevia the atlas localizes the more difficult to locate and segment tissueof interest. An example of such a structure is bone such as ribs,vertebrae, etc., and examples of such tissue include lung lobes,bronchi, vessels, kidney, liver, heart, etc.

The illustrated multi-structure atlas generator 104 obtains structuralimage data from a CT scanner 108 and/or a MR scanner 110 and/or a datarepository 112. Other suitable scanners include, but are not limited to,an x-ray, ultrasound, a combination thereof (including hybrid scannerwith multiple modalities), etc. scanner. The data repository 112 mayinclude a picture archiving and communication system (PACS), a radiologyinformation system (RIS), a hospital information system (HIS), and/orthe like.

The multi-structure atlas can be conveyed to one or more output devices122 such as a display monitor for visual presentation, storage medium, afilmer, a printer, the image data processor 106, etc. One or more inputdevices 124 allows a human and/or machine (e.g., robot) user to interactwith the multi-anatomical structure atlas generator 104, for example, tofacilitate selecting image data for processing, identifying structurefor the atlas, segmenting structure and/or tissue of interest, etc.

The multi-structure atlas generator 104 can generate one or moremulti-structure atlases. For example, the multi-structure atlasgenerator 104 can generate a multi-structure atlas based on subject age,size, weight, gender, and/or other characteristics. In another example,the multi-structure atlas generator 104 can generate a single generalmulti-structure atlas that is independent of subject age, size, weight,gender, and/or other characteristics.

The image data processor 106 includes a structural image data processor114 and a functional image data processor 116. In a variation, at leastone of the structural image data processor 114 or the functional imagedata processor 116 is not part of the image data processor 106.

The structural image data processor 114 is configured to at leastsegment predetermined tissue of interest from structural image dataunder evaluation based on the multi-structure atlas. As described ingreater detail below, in one instance, this may include localizing aregion of interest that includes the tissue of interest in structuralimage data based on the multi-structure atlas, and segmenting the tissueof interest from the region of interest in the structural image data.

Likewise, the structural image data can be obtained from the CT scanner108, the MR scanner 110, a combination thereof (including a hybridscanner), other scanner, the data repository 112, etc. However, in thisinstance, the structural image data is from an examination underevaluation. The predetermined tissue of interest can be identified basedon the segmentation algorithm employed, a default configuration file, auser input, and/or otherwise.

The segmented predetermined tissue of interest can be conveyed to one ormore output devices 126 such as a display monitor for visualpresentation, storage medium, a filmer, a printer, the functional imagedata processor 116, etc. One or more input devices 128 allows a humanand/or machine (e.g., robot) user to interact with the image dataprocessor 106. Such interaction may include providing an input thatidentifies the tissue of interest, a segmentation algorithm, etc.

The functional image data processor 116 is configured to at leastidentify the predetermined tissue of interest in functional image data.As described in greater detail below, in one instance this includesemploying the structural image data, the structural image dataregistered with the functional image data, and the segmentedpredetermined tissue of interest, which was segmented from thestructural image data, to identify the predetermined tissue of interestin functional image data.

The functional image data can be obtained from the CT scanner 108, theMR scanner 110, a SPECT scanner 118, a PET scanner 120, a combinationthereof (including a hybrid scanner) and/or other functional imagingscanner, the data repository 112, etc. The functional image dataprocessor 116 can also process the identified tissue of interest in thefunctional image data and quantify functional information correspondingthereto.

The functional image data with the identified tissue of interest and/orthe quantified functional information (e.g., indicia indicative of thequantification) can be conveyed to the one or more output devices 126.This can be concurrent with the display of image data or not. The one ormore input devices 128 allows a human and/or machine (e.g., robot) userto interact with the image data processor 106. Such interaction mayinclude providing an input that identifies the tissue of interest, aregistration algorithm, a quantification algorithm, etc.

A suitable CT scanner 108 includes a stationary gantry and a rotatinggantry, which is rotatably supported by the stationary gantry androtates around an examination region about a z-axis. A radiation source,such as an x-ray tube, is rotatably supported by the rotating gantry,rotates with the rotating gantry, and emits radiation that traverses theexamination region. A radiation sensitive detector array subtends anangular arc opposite the radiation source across the examination region.The detector array detects radiation traversing the examination regionand generates projection data indicative thereof. A reconstructorreconstructs the projection data, generating 3D volumetric image data.

A suitable MR scanner 110 includes a main magnet, gradient (x, y, and z)coils, and a RF coil. The main magnet (superconducting, resistive, orpermanent) produces a substantially homogeneous, temporally constantmain magnetic field B₀ in the examination region. The gradient coilsgenerate time varying gradient magnetic fields along the x, y, andz-axes of the examination region. The RF coil produces radio frequencysignals (at the Larmor frequency of nuclei of interest (e.g., hydrogen,etc.)) that excite the nuclei of interest in the examination region andreceive MR signals emitted by the excited nuclei. A MR data acquisitionsystem processes the MR signals, and a MR reconstructor reconstructs thedata and generates MR images.

A suitable SPECT scanner 118 includes a gamma radiation detector and acollimator, which is disposed between an examination region and thegamma radiation detector. The collimator includes radiation attenuatingsepta that only allow gamma radiation having a certain angle ofincidence to reach the gamma detector. Gamma rays are acquired from anumber of angles with respect to the examination region by rotating thegamma radiation detector around the examination region. The detectorgenerally is positioned close to the subject under evaluation. A SPECTreconstructor reconstructs the projections to produce volumetric datarepresentative of the distribution of the radioisotope emitting thegamma rays in the object or subject.

A suitable PET scanner 120 includes a ring of gamma radiation detectorsarranged around an examination region. The detectors are configured todetect 511 keV gamma rays indicative of electron-positron decaysoccurring in an examination region. Most decays result in two 511 keVgamma rays emitted almost 180 degrees to each other, and PET scannerslocalize the source along a line of response (LOR) there between. Thedetectors convert the photons into a corresponding electrical signal,and a coincidence event identifier identifies coincident gamma pairs byidentifying photons detected in temporal coincidence. The identifiedpairs are used to generate data indicative of the spatial distributionof the decays.

Turning to FIG. 2, an example of the multi-structure atlas generator 104is illustrated. As discussed above, the multi-structure atlas generator104 obtains and processes structural image data for producing atlases.Generally, such image data is from prior scans, not currently underevaluation, from multiple different subjects, and covering thestructure/region and the tissue of interest.

An image data selector 202 selects all or a sub-set of the obtainedstructural image data for processing. A suitable sub-set may includeimage data for a sub-set of the subjects and/or image data correspondingto particular demographics (e.g., weight, size, age, gender, etc.). Theimage data selector 202 selects the anatomical image data with orwithout user interaction. In a variation, the image data selector 202 isomitted, and either all of the obtained image data is utilized oranother approach is employed to select a sub-set of the image data.

A tissue of interest segmentor 204 segments predetermined tissue ofinterest from each of the selected image data. In the illustratedembodiment, the predetermined tissue of interest represents tissue ofinterest that is not as well-suited for automated segmentationtechniques, or tissue of interest that is relatively more difficult tolocate and segment. Generally, this segmentation is performed with userinteraction. Known and/or other segmentation algorithms can be employed.

A structure or region of interest segmentor 206 segments a predeterminedstructure of interest from each of the selected image data. In theillustrated embodiment, the predetermined structure of interestrepresents structure that is well-suited for automated segmentationtechniques, or structure that is relatively easier to locate andsegment, and that spatially maps to the tissue of interest. Known and/orother segmentation algorithms can be employed by the structure ofinterest segmentor 206, typically without user interaction.

A structure to tissue mapper 208 spatially or physically maps (orgenerates a map or mapping), for each sub-set of image data, thesegmented structure and the segmented tissue of interest based on theiranatomical spatial context in the image data. Thus, the spatialrelationship between the structure and the tissue of interest in theoriginal image data is maintained. The mapping is such that a particularsub-portion of the structure is physically at or near the tissue ofinterest such that locating the particular sub-portion identifies theregion in which the tissue of interest is most likely to be found.

A modeler 210 generates the multi-structure atlas based on the mappingsfor all (or a sub-set of) the sub-sets of image data. The atlas includesboth the structure and the tissue of interest, and the spatialrelationship or physical mapping there between. In one instance, thisincludes generating the atlas based on an average of the mappings. Inanother instance, this includes using the mapping that occurs with thegreatest frequency. In yet another instance, this includes utilizing auser to identify one of the mappings.

Next at FIG. 3, an example of the image data processor 106 isschematically illustrated.

The structural image data processor 114 includes an atlas based regionof interest (ROI) segmentor 302. The atlas based ROI segmentor 302segments an ROI from structural image data under evaluation based on themulti-structure atlas. For example, the atlas based ROI segmentor 302segments a ROI that includes structure from the atlas that physicallymaps to the location of the tissue of interest in the atlas.

This may include registering the structural image data and themulti-structure atlas using either a non-rigid or a rigid registrationalgorithm. The ROI can then be extracted from the registered structureddata either automatically or with user intervention. The ROI can be inany form, such as a sub volume, a point cloud, a 3D sheet of a giventhickness or any other 3D structure. The ROI provides a localized regionthat includes the tissue of interest.

The structural image data processor 114 also includes a tissue ofinterest segmentor 304. The tissue of interest segmentor 304 receivesthe ROI segmented by the atlas based ROI segmentor 302. The tissue ofinterest segmentor 304 segments the predetermined tissue of interestfrom the segmented ROI. As discussed herein, segmenting the tissue ofinterest from the localized ROI facilitates accurate segmentation of thetissue of interest, which might otherwise be difficult to locate andsegment from the entire structural image data. Likewise, thesegmentation can be automated and/or include user interaction.

The functional image data processor 116 includes a registrationcomponent 306, a tissue of interest identifier 308, and a functionalcharacteristic quantifier 310. The registration component 306 registersthe functional image data under evaluation with the structural imagedata under evaluation. The tissue of interest identifier 308 identifiesthe tissue of interest in the functional image data based on thefunctional image, the registered image data and the segmented tissue ofinterest. As discussed herein, this facilitates accurate identificationof the tissue of interest in the functional image data.

The functional characteristic quantifier 310 quantifies one or morefunctional characteristics from the identified tissue of interest in thefunctional image data. Such quantification may correspond to a radiationactivity in the tissue of interest, a perfusion in the tissue ofinterest, etc. Although shown as part of the functional image dataprocessor 116, in a variation, the functional characteristic quantifier310 can be part of a different component, including another component ofthe image data processor 106 and/or a component external to the imagedata processor 106.

As discussed above, the image data processor 106 can employ the outputdevice(s) 122, and where the output device(s) is a display monitor, theimage data processor 106 can visually present one or more of thesegmented tissue of interest from the anatomical image data, theidentified tissue of interest in the functional image data, and/or theone or more quantified functional characteristics via the outputdevice(s) 122.

FIG. 4 illustrate methods for generating a multi-structure atlas. It isto be appreciated that the ordering of the acts is not limiting. Assuch, other orderings are contemplated herein. In addition, one or moreacts may be omitted and/or one or more additional acts may be included.

For explanatory purposes and sake of brevity the method is discussed inthe context of lung lobe evaluation based on a rib cage/lung lobe atlas,where ribs correspond to the structure easier to locate and the lunglobe corresponds to the tissue of interest. Generally, ribs are staticbone structure relative to and surrounding the lungs, do not change muchwith breathing motion, and are fairly easy to segment in structuralimage data.

At 402, a set of structural image data, including image data of the ribcage, is obtained. As discussed above, the image data may correspond todifferent people and a plurality of exams and can be obtained from ascanner and/or a data repository.

At 404, the set or a sub-set of the image data is identified for furtherprocessing. The sub-set of image data can be based on demographics suchas age, weight, size, gender, and/or other characteristics.

At 406, the rib cage is segmented from the image data for each exam.Generally, this can be achieved through automated segmentationtechniques as the rib cage represents easier to locate and segmentanatomy.

At 408, the lung lobes are segmented from the image data for each exam.Generally, this can be achieved through manual and/or semi-automatedsegmentation techniques as the fissures between lung lobes and lung lobeboundaries can be difficult to locate and segment. Semi-automatic andmanual lung lobe segmentation technique may result in identifying thefissure location of each lobe at the boundary of the lungs in 3D or thefissure location across the lungs in 3D. The former will result in a 3Dpoint cloud (of lobe fissures at the surface of the lungs), whereas thelatter will result in a 3D surface across the lungs (representing thelobe fissure boundaries). Fissure identification can be performed in 3Dor on 2D slices that are later merged into 3D volume.

At 410, a map (or mapping), for each examination, between rib locationand lung lobe location is generated based on the segmented rib cageimage data and the segmented lung lobes.

By way of example, for one examination, the mapping may indicate that alung lobe boundary is located between ribs M and N, where M and N areintegers. Another examination may indicate the same. Yet anotherexamination may indicate the lung lobe boundary is between a next pairof ribs.

In a variation, the atlas may generate the ROI that has a set of 3Dpoints representing the point cloud on the lung boundary with a highprobability of having fissure boundary on the lung surface, and/or a 3DROI across the lung having a high probability of having the fissureboundary across the lung.

For the one examination, the mapping may also indicate that a lung lobeboundary is located a distance X from rib M and a distance Y from rib N,where X and Y are integers. For the other examination indicating thesame pair of ribs, the mapping may indicate that a lung lobe boundary islocated a distance I from rib M and a distance J from rib N, where I andJ are integers and are different from X and Y.

The locations of the lung lobe boundary may also be mapped in terms ofphysical spacing e.g. lung lobe boundary on the surface of the lung maybe located within a range (e.g., X₁±Y₁ millimeters from rib G, and X₂±Y₂millimeters from rib H), where G lies above rib H, or vice-versa. Inanother scenario, the atlas may also result in a mapping of an Lmillimeter thick 3D ROI (in form of a 3D sheet) with a high probabilityof having fissures across the lung volume

At 412, a rib cage/lung lobe atlas is generated based on the mappingsfor all or a sub-set of the exams. As discussed herein, the atlas may bebased on an average mapping, a most frequently occurring mapping, a userselected mapping and/or other mapping. For example, for averaging, usingthe above example, mapping may indicate that the rib M is a distance(X+I)/2 from the boundary and the rib N is a distance (Y+J)/2 from theboundary.

FIG. 5 illustrate methods for utilizing the multi-structure atlas ofFIG. 4 for segmentation. It is to be appreciated that the ordering ofthe acts is not limiting. As such, other orderings are contemplatedherein. In addition, one or more acts may be omitted and/or one or moreadditional acts may be included.

Continuing with the example in FIG. 4, with this example, the ribcage/lung lobe atlas is used to localize lung lobes for segmentation instructural image data and the segmented lung lobes are used to identifythe lung lobes in functional image data.

At 502, structural image data, including image data of a chest of asubject, to evaluate is obtained.

At 504, functional image data, including image data of a chest of asubject, to evaluate is obtained.

At 506, a multi-structure atlas including the rib cage with riblocations mapped to the lung lobes is obtained. As discussed herein, theatlas may be generic or specific based on subject age, weight, size,gender, etc.

At 508, a region of interest (ROI) that includes the lung lobes issegmented based on the atlas.

With continuance of the example in FIG. 4, if the lung lobe boundarylocated between ribs M and N is the tissue of interest, then the ROI isbased on the location of the ribs M and N in the atlas such that the ROIincludes the boundary of interest.

An example of such an atlas is shown in FIG. 6. In FIG. 6, an atlas 600includes a lung lobe 602 with a first lobe 604 and a second lobe 606,separated at a boundary 608 that is located between a first rib 610 anda second rib 612.

With this atlas, the boundary 608 is localized based on the first andsecond ribs 610 and 612, and a ROI 614 may include a region around theribs 610 and 612 (as shown) and/or between the ribs 610 and 612. Otherribs and lung structure are omitted from the atlas 600 for claritypurposes.

At 510, the lung lobes are segmented from the ROI.

At 512, the structural image data and the functional image data areregistered.

At 514, the lung lobes are identified in the functional image data basedon the registered image data and the lung lobes segmented from thestructural image data.

At 516, optionally, a functional characteristic of the identified lunglobes in the functional image data is quantified.

The above methods may be implemented by way of computer readableinstructions, encoded or embedded on computer readable storage medium,which, when executed by a computer processor(s), cause the processor(s)to carry out the described acts. Additionally or alternatively, at leastone of the computer readable instructions is carried by a signal,carrier wave or other transitory medium.

Although FIGS. 4 and 5 are described in connection with identifying lunglobes using a rib cage/lung lobe atlas, it is to be understood that thisatlas and/or this tissue of interest (lung lobes in FIGS. 4 and 5) arenot limiting. For example, the tissue can be kidney, heart, liver,bronchi, a vessel, and/or other tissue where the rib cage can beutilized as a spatial reference. Furthermore, the structure can be otherstructure such as the spine where individual vertebrae, instead of ribs,are utilized as spatial anatomical landmarks for the lung lobes. In yetanother instance, both the structure and the tissue of interest can bedifferent.

The invention has been described with reference to the preferredembodiments. Modifications and alterations may occur to others uponreading and understanding the preceding detailed description. It isintended that the invention be constructed as including all suchmodifications and alterations insofar as they come within the scope ofthe appended claims or the equivalents thereof.

1. An image data processor, comprising: a structural image dataprocessor that employs a multi-structure atlas to segment a region ofinterest from structural image data which includes tissue of interestand that segments the tissue of interests from the region of interest,wherein the structure is rib cage and the tissue of interest is lunglobe, and the multi-structure atlas is a rib cage/lung lobe atlas thatincludes a mapping that maps one or more ribs to a location of aboundary of a lung lobe, and the mapping includes a lung lobe region ofinterest, in a form of a least one of a point cloud on a surface of thelung or a 3D sheet across lung, through the rib case/lung lobe atlas;and a functional image data processor that identifies the tissue ofinterest in functional image data based on the segmented tissue ofinterest.
 2. The image data processor of claim 1, wherein the functionalimage data processor further registers the structural image data and thefunctional image data and identifies the tissue of interest in thefunctional image data based on the segmented tissue of interest and theregistered image data.
 3. The image data processor of claim 1, whereinthe functional image data processor further quantifies functionalinformation in the identified tissue of interest in the functional imagedata.
 4. The image data processor of claim 1, wherein themulti-structure atlas includes a mapping that physically maps locationsof sub-structure of the structure of interest to the tissue of interestfor each sub-image data of the image data corresponding to at least oneof a different examination or a different subject, and the structuralimage data processor segments the region of interest based on thesub-structure in the atlas so that the region of interest includes thetissue of interest.
 5. The image data processor of claim 4, wherein themapping includes at least one of 1) a point cloud of the tissue ofinterest on or within an organ surface; 2) a 3D sheet having the tissueof interest across an organ; 3) or a 3D structure in a form of region ofinterest.
 6. The image data processor of claim 4, further comprising: amulti-structure atlas generator that generates the multi-structureatlas, the multi-structure atlas generator, comprising; a structure ofinterest segmentor that automatically segments the structure from thestructural image data based on a predetermined algorithm; a tissue ofinterest segmentor that segments the tissue of interest from thestructural image data based on user input; a structure to tissue mapperthat generates a map mapping a physical spatial location of eachsub-structure to the segmented tissue of interest; and a modeler thatgenerates the multi-structure atlas based on a plurality of the maps. 7.(canceled)
 8. An image data processor, comprising: a multi-structureatlas generator that generates a multi-structure atlas, wherein themulti-structure atlas physically maps structure of interest to tissue ofinterest such that locating the structure of interest in structuralimage data based on the multi-structure atlas localizes the tissue ofinterest to the region of interest.
 9. The image data processor of claim8, the multi-structure atlas generators comprising; a structure ofinterest segmentor that automatically segments the structure from thestructural image data based on a predetermined algorithm; a tissue ofinterest segmentor that segments the tissue of interest from thestructural image data with user interaction; a structure to tissuemapper that maps a physical spatial location of the segmented structureto the segment tissue of interest; and a modeler that generates themulti-structure atlas based on a plurality of maps, wherein each mapcorresponds to sub-set of the image data for at least one of a differentexamination or a different subject.
 10. The image data processor ofclaim 9, further, comprising: a structural image data processor thatemploys the multi-structure atlas to segment a region of interest fromstructural image data under evaluation which includes the tissue ofinterest and that segments the tissue of interests from the region ofinterest; and a functional image data processor that registers thestructural image data and the functional image data and that identifiesthe tissue of interest in the functional image data based on thesegmented tissue of interest and the registered image data.
 11. Theimage data processor of claim 8, the functional image data processorfurther quantifies functional information in the identified tissue ofinterest.
 12. A method, comprising: employing a multi-structure atlas tosegment a region of interest from structural image data which includestissue of interest and that segments the tissue of interests from theregion of interest, wherein the structure is rib cage and the tissue ofinterest is lung lobe, and the multi-structure atlas is a rib cage/lunglobe atlas that includes a mapping that maps one or more ribs to alocation of a boundary of a lung lobe, and the mapping includes a lunglobe region of interest, in a form of a least one or a point cloud on asurface of the lung or a 3D sheet across lung, through the rib case/lunglobe atlas; and identifying the tissue of interest in functional imagedata based on the segmented tissue of interest.
 13. The method of claim12, further comprising: registering the structural image data and thefunctional image data and identifying the tissue of interest in thefunctional image data based on the segmented tissue of interest and theregistered image data.
 14. The method of claim 12, further comprising:quantifying functional information in the identified tissue of interest.15. The method of claim 12, wherein the multi-structure atlas physicallymaps structure to the tissue of interest for each sub-image data of theimage data corresponding to at least one of a different examination or adifferent subject, and the structural image data processor segments theregion of interest based on the structure in the atlas so that theregion of interest includes the tissue of interest.
 16. The method ofclaim 15, further comprising: automatically segmenting the structurefrom the structural image data based on a predetermined algorithm;segmenting the tissue of interest segmentor from the structural imagedata under user interaction; mapping a physical spatial location of thesegmented structure to the segment tissue of interest; and generatingthe multi-structure atlas based on a plurality of mappings, eachcorresponding to a different sub-set of the image data.
 17. A method,comprising: generating a multi-structure atlas, wherein themulti-structure atlas physically maps structure to tissue of interestsuch that locate the structure in structural image data based on themulti-structure atlas localizes the tissue of interest to the region ofinterest.
 18. The method of claim 17, further comprising; automaticallysegmenting the structure from the structural image data based on apredetermined algorithm; segmenting the tissue of interest segmentorfrom the structural image data under user interaction; mapping aphysical spatial location of the segmented structure to the segmenttissue of interest; and generating the multi-structure atlas based on aplurality of mappings, each corresponding to a different sub-set of theimage data.
 19. The method of claim 17, further comprising: employing amulti-structure atlas to segment a region of interest from structuralimage data that includes tissue of interest and that segments the tissueof interests from die region of interest; and registering the structuralimage data and the functional image data; and identifying the tissue ofinterest in the functional image data based on the segmented tissue ofinterest and the registered image data.
 20. The method of claim 17,further comprising: quantifying functional information in the identifiedtissue of interest.
 21. The image data processor of claim 1, wherein, inresponse to a lung lobe boundary being located between a first rib and asecond different rib in the tissue of interest, the functional imagedata processor identifies the region of interest from a location of thefirst and second ribs in the multi-structure map, and the region ofinterest includes the lung lobe boundary
 22. The image data processor ofclaim 21, wherein the functional image data processor localizes the lunglobe boundary based on the first and second ribs.
 23. The image dataprocessor of claim 21, wherein the functional image data processordetermines the region of interest includes s region around the first andsecond ribs.
 24. The image data processor of claim 21, wherein thefunctional image data processor determines the region of interestincludes a region between the first and second ribs.
 25. The image dataprocessor of claim 21, wherein the multi-structure map indicates a lunglobe boundary is located a first distance from the first rib and asecond distance from the second rib.
 26. The image data processor ofclaim 21, wherein the multi-structure map includes a numerical valuethat indicates a physical spacing of the lung lobe boundary from a ribon a surface of the lung.
 27. The image data processor of claim 1,wherein the multi-structure map indicates a thickness of the region ofinterest, in a form of the 3D sheet, with fissures across a lung volume.28. The image data processor of claim 1, wherein the point cloudincludes a set of 3D points on the lung boundary.